Feature based image processing methods applied to bathymetric measurements from airborne remote sensing in fluvial environments.

Abstract

Bathymetric maps produced from remotely sensed imagery are increasingly common. However, when this method is applied to fluvial environments, changing scenes and illumination variations severely hinder the application of well established empirical calibration methods used to obtain predictive depth-colour relationships. In this paper, illumination variations are corrected with feature based image processing, which is used to identify areas in an image with a near-zero water depth. This information can then be included in the depth-colour calibration process, which results in an improved prediction quality. The end product is an automated bathymetric mapping method capable of a 4 m2 spatial resolution with a precision of ±15 cm, which allows for a more widespread application of bathymetric mapping.